Using transaction data to predict vehicle depreciation and present value

ABSTRACT

Various embodiments are directed to a system or platform with machine learning capabilities configured to accurately predict in real-time a depreciation factor of a vehicle associated with a customer and further accurately predict a present value of the vehicle based at least in part on card transaction data associated with the customer. Based on one or more factors, such as a determination that the present value of the vehicle falls below a predefined threshold value, one or more auto financing products may be generated and provided to the customer by the system.

BACKGROUND

A financial institution (e.g., bank) may have an automobile financingarm that may provide various automobile financing (hereinafter “autofinancing”) products to customers. For example, an auto loan for apredetermined amount at a predetermined rate may be provided to aqualified customer. In another example, refinancing on an existing loanmay be offered to the customer.

Besides what type of vehicle a customer purchased, when, and for howmuch, the auto financing arm of the financial institution typically doesnot know or retain more information about the vehicle. In at least thatregard, the auto financing arm has no way of knowing whether thecustomer might be in the market for a new vehicle at some point in timeor how much the vehicle is presently worth.

SUMMARY

Various embodiments are generally directed to a system or platform withmachine learning capabilities configured to accurately predict inreal-time a depreciation factor of a vehicle associated with a customerand further accurately predict a present value of the vehicle based atleast in part on card transaction data associated with the customer.Based on one or more factors, such as a determination that the presentvalue of the vehicle falls below a predefined threshold value, one ormore auto financing products may be generated and provided to thecustomer by the system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an vehicle depreciation prediction system inaccordance with one or more embodiments.

FIG. 2 illustrates an example machine learning model in accordance withone or more embodiments.

FIG. 3 illustrates an example card statement in accordance with one ormore embodiments.

FIG. 4 illustrates an example calculation of miles driven in accordancewith one or more embodiments.

FIG. 5 illustrate example products in accordance with one or moreembodiments.

FIG. 6 illustrates an example flow diagram in accordance with one ormore embodiments.

FIG. 7 illustrates an example computing architecture of a computingdevice in accordance with one or more embodiments.

FIG. 8 illustrates an example communications architecture in accordancewith one or more embodiments.

DETAILED DESCRIPTION

Various embodiments are generally directed to a system or platform withmachine learning capabilities configured to accurately predict inreal-time a depreciation factor of a vehicle belonging to a customerand, thus, accurately predict a present value of the vehicle based atleast in part on card transaction data associated with the customer.

In examples, the system may be configured to identify a customer thathas both a card account (e.g., credit card, debit card, charge card,virtual card, etc.) and an auto financing account. For that customer,card transaction data may be analyzed to identify fuel station (e.g.,gas, diesel) expenses. Along with information related to themake/model/year of the vehicle, the average miles per gallon (MPG)corresponding to the vehicle, and cost of gas on the days correspondingto the gas station expenses at the respective gas stations, the systemcan accurately determine or predict a number of miles the vehicle hasdriven within a predefined time frame (e.g., miles driven per year,miles driven per month).

Based on the miles-driven calculation, the system may then accuratelydetermine a depreciation factor (e.g., $0.25 per driven mile, $0.50 perdriven mile) and also accurately determine the present value of thevehicle. The machine learning component of the system (e.g., a machinelearning model) may not only perform one or more of the aforementionedcalculations, determinations, predictions, etc. but may intelligentlykeep track of and adjust various variables associated with the vehicledepreciation and present value calculations. For example, as will befurther described below, the system may set the first calculateddepreciation factor a “default” depreciation factor, and each time thevehicle is sold or involved in a particular transaction, the machinelearning component of the system may adjust or update the factor basedon newly acquired data associated with the sale or transaction (e.g.,estimated sale value versus actual sale value).

In further examples, upon predicting or determining an accuratedepreciation factor and a present value for the vehicle, the system mayalso determine whether the customer qualifies or whether it is timely topresent to the customer any vehicle financing products (e.g., a loan orfinancing offer for a new vehicle, refinancing offer) based on thepresent value of the vehicle. If the customer qualifies or if the systemdetermines that it is timely (e.g., the vehicle has reached a thresholdage or mileage), the system may generate and provide the one or moreproducts to the customer via one or more channels (e.g., e-mail, textmessage, application notification message).

Previously, it was extremely difficult, or even impossible, for an autofinancing department of a financial institution to know, derive, orobtain accurate vehicle-related information associated with a customer,such as its present value, without customer input. The embodiments andexamples described herein overcome the above problems and areadvantageous over the previous solutions in that a current value of avehicle can be accurately predicted in real-time or near real-time andwithout input by the customer by analyzing customer transaction data andcalculating a depreciation factor associated with the vehicle. Anotheradvantage of the embodiments and examples described herein, for example,is that a machine learning component can continually adjust thevariables used to determine vehicle depreciation factor based onreal-world data and further training of the machine learning component.In at least that regard, the machine learning component allows thesystem to constantly learn and evolve to better and more accuratelypredict vehicle depreciation and value over a period of time.

Reference is now made to the drawings, where like reference numerals areused to refer to like elements. In the following description, for thepurpose of explanation, numerous specific details are set forth in orderto provide a thorough understanding thereof. It may be evident, however,that the novel embodiments can be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form to facilitate a description thereof. The intentionis to cover all modification, equivalents, and alternatives within thescope of the claims.

FIG. 1 illustrates an example vehicle deprecation prediction system 100according to one or more embodiments. As will be further discussedbelow, one or more computing devices (e.g., server computers, laptops,smartphones, tablet computers, etc.), and/or processing circuitriesthereof, may be operable to execute instructions that execute, run,and/or provide support to the system 100 and the various componentstherein. As shown, the vehicle depreciation prediction system 100 mayinclude at least a customer identification engine 102, a filter engine104, a vehicle information engine 106, a machine learning component 110,which may include at least a distance calculation engine 111 and adepreciation calculation engine 112, and a product engine 116, each ofwhich will be further described below.

In examples, customer accounts and information associated therewith maybe input to or received by the customer identification engine 102. Insome examples, the customer identification engine 102 may continuallyobserve and analyze the customer accounts. In one embodiment, thecustomer identification engine 102 may identify or determine allcustomers that have both at least one card account (e.g., credit card,debit card, charge card, virtual card) and at least one vehiclefinancing account (e.g., account associated with a vehicle loan). Tothat end, the system 100 may be able to at least identify a specific setof target customers—e.g., customers that are both card andauto-financing customers.

Upon identifying at least one customer that has both a card account anda vehicle financing account, transaction data from the card account maybe input to (or received or accessed by) the filter engine 104 foridentifying or filtering out specific transactions that signify orindicate a gas station expense. For example, gas station expenses may bedetermined by way of merchant category codes (MCCs). In another example,a text-based search or an optical character recognition (OCR) proceduremay be performed on the card transactions to determine gas stationexpenses. In yet a further example, the machine learning component 110,which may be a machine learning model, may be utilized to analyze whichof the transactions are gas station expenses. It may be understood thattransaction patterns (e.g., recurrence of specific values or amounts atspecific times) may also indicate gas expenses. It may further beunderstood that the filter engine 104 or other components of the system100 may have mechanisms to subtract any non-gas related expenses thatmay be incurred at gas stations, such as food or snack purchases.

As further shown in FIG. 1 , gas expenses filtered or identified by thefilter engine 104 may be input to (or received or accessed by) thedistance calculation engine 111 of the machine learning component 110.Moreover, information associated with the customer's vehicle from thevehicle information engine 106 may be input to (or received or accessedby) the distance calculation engine 111. In examples, the vehicleinformation engine 106 may determine or identify the exact vehicle(e.g., make/model/year 107) that the customer has purchased or financed,for instance, based at least on internal or external databases orcustomer account transaction data (e.g., if the customer purchased thevehicle via a customer account, such as checking, credit, savings). Infurther examples, the vehicle information engine 106 may obtain oraccess MPG-related information associated with the make/model/year 107of the vehicle, which may be passed on to the distance calculationengine 111. It may be understood that information related tomake/model/year 107 of the vehicle and MPG 108 may be provided by ororiginate from external or third-party sources.

The vehicle depreciation prediction system 100 may also determine and/orobtain the exact gas prices corresponding to the specific dates and gasstations of the respective gas station expenses determined by the filterengine 104. As will be further described below, the gas prices alongwith the MPG information associated with the make/model/year of thevehicle may be used to calculate the miles driven on the vehicle, andthus, the depreciation factor and/or the present value of the vehicle.It may also be understood that the gas prices for the various days andlocations associated with the gas station expenses may be provided byexternal or third-party sources.

As illustrated, the distance calculation engine 111 of the machinelearning component 110 may receive at least the gas expenses from thefilter engine 104, the MPG 108 from the vehicle information engine 106,and cost of gas 109. Using this information, the distance calculationengine 111 may determine a number of miles that were driven on thevehicle for a predetermined or predefined period of time (e.g., milesper year, miles per month, miles per week). It may be understood thatthe distance calculation may be, in some instance, limited to real,ascertainable transaction data available in the customer's cardstatements. In other instances, the distance calculations may beprojections or estimations based on the calculations from theascertainable data.

The calculated number of miles driven on the vehicle may be provided tothe depreciation calculation engine 112, which may determine adepreciation factor associated with the vehicle based on the calculatedusage rate. For example, the depreciation calculation engine 112 maydetermine that the depreciation factor is twenty-five cents per drivenmile (e.g., the vehicle depreciates by $0.25 for every mile driven). Itmay be understood that the depreciation factor is dynamic and may changebased on the usage rate of the vehicle and/or changing market value ofthe vehicle. As will be further described below, the machine learningcomponent 110 may adjust depreciation factor calculations based onreal-world data it receives when a vehicle transaction event occurs,such as the vehicle being sold, traded, etc.

Thereafter, the vehicle depreciation factor may be used to accuratelypredict a present value of the vehicle 114, which may be output by thesystem 100 and/or input to a product engine 116. As shown and as will befurther described below, the product engine 116 may determine that,based on the vehicle value 114, the customer is in the market for a newvehicle, refinancing, or the like. For example, the product engine 116of the system 100 may determine that vehicle value 114 has dropped belowa predetermined threshold value, which may trigger the engine 116 togenerate and output certain products 118 to the customer. Inembodiments, the products 118 may be advertising products, such as afinancing offer (e.g., new loan, new financing) so that the customer canpurchase a new vehicle. As will be further described below, the products118 may be delivered via one or more channels, e.g., e-mail, textmessage, app notification message, website banner advertisement, etc.

It may be understood that the system 100 and the components therein arearranged in the manner depicted for illustrative purposes. Accordingly,the arrangement of the components of the system 100 is not limited toany particular manner. For example, the machine learning component 110may encompass more than just the distance calculation engine 111 and thedepreciation calculation engine 112, and in some examples, the machinelearning component 110 may be integrated into the entire system 100 andthe other components thereof.

FIG. 2 illustrates an example machine learning model 200 and trainingthereof according to one or more embodiments. The machine learning model200 may be implemented as the machine learning component 110 of FIG. 1 ,as described above. It may be understood that the machine learning model200 may be any type of learning algorithm, such as a supervised learningalgorithm, an unsupervised learning algorithm, reinforcement learningalgorithm, and may include at least a linear regression model, logisticregression model, a decision tree model, a support vector machine (SVM)model, a Naive Bayes model, a k-nearest neighbors (kNN) model, k-meansmodel, a random forest model, a dimensionality reduction algorithm, agradient boosting algorithm, an XGBoost algorithm, a LightGBM algorithma Catboost model, etc. The machine learning model 200 may also encompassclassification models, one or more of which may be based on aconvolutional neural network (CNN) algorithm, a recurrent neural network(RNN) algorithm, a hierarchical attention network (HAN) algorithm, orthe like.

As shown, the machine learning model 200 may be trained using one ormore training data sets or batches that include at least three differentdata types. For example, a first data type may be customer transactiondata 202. In another example, a second data type may be data fromthird-party sources 204. In yet another example, a third data type maybe real-world data 206.

The customer transaction data 202 may include any data associated withcard-based transactions, such as credit card transactions, debit cardtransactions, charge card transactions, virtual card transactions, andthe like. Moreover, the customer transaction data 202 may includevarious types of transactional information (e.g., MCC or any other typesof terms, descriptors, identifiers, numbers) as normally found, forinstance, on a card statement. In the transaction data 202, gas-relatedexpenses may be flagged for training the machine learning model 200. Theexpenses may be flagged based on certain MCCs, certain descriptors, etc.In at least that regard, the model 200 may be configured to receive acustomer transaction data set and accurately determine whichtransactions are gas expenses.

In embodiments, data 204 from third-party sources may include varioustypes of information related to vehicles, such as the make, model, andyear corresponding to the vehicles, MPG information with respect tothose vehicles, vehicle values set by third-party industry guides.Moreover, the third-party data 204 may also include cost of gas forvarying octane levels and cost of diesel corresponding to predefinedtime periods and/or specific dates. The machine learning model 200 maybe trained to recognize one or more portions of the third-party data 204as important pieces of information in calculating and predicting atleast a depreciation factor and a present value of the vehicle.

In further embodiments, the real-world data 206 may include informationrelating to real transactional events associated with the vehicle, suchas the sale of the vehicle (e.g., actual price sold), parties involvedin the sale, a trade-in event, etc. The real-world data 206 may be usedby the machine learning model 200 to intelligently adjust depreciationand present-value calculations based on the real-life transactions. Inat least that way, the actual sale price of the vehicle, for example,can be compared to the predicted value and, based on this comparison,the depreciation factor or the present value calculations can beautomatically adjusted by the model 200. For purposes of training themachine learning model 200, the real-world data 206 may be simulated andflagged so that the model 200 can learn how to adjust or modify suchcalculations.

As shown in FIG. 2 , the output of the machine learning model 200 may beat least depreciation data 208 and present value data 210, which may beused to generate one or more auto financing products, as described abovewith respect to the product engine 116 of FIG. 1 . It may be understoodthat training the machine learning model 200 may broadly involve atleast learning (e.g., determining) good values for all the weights andthe bias from labeled examples and the broader goal of training themodel may be to find a set of weights and biases that result in low loss(e.g., penalty for a bad prediction), on average, across all examples.For instance, in supervised learning, a machine learning algorithm maybuild a model by examining many examples and attempting to find a modelthat minimizes loss.

FIG. 3 illustrates an example card statement 300 according to one ormore embodiments. In examples, transaction data from the card statement300 may be analyzed, for example, by the filter engine 104 of thevehicle depreciation prediction system 100 described above, to identifyor filter out any gas expenses to be used for the vehicle depreciationand value predictions. In examples, the card statement 300 may be amonthly credit card statement for customer, John Doe, containing allcredit card expenses from Jan. 13, 2020 to Feb. 12, 2020.

As shown, out of all the expenses occurred during the month, whichincludes, among other expenses, food and entertainment expenses, fourseparate gas station expenses can be identified: an expense of $35.56 onJanuary 13 at Gas Station #456, an expense of $32.62 on January 21 atGas Station #571, an expense of $33.50 on January 28 at Gas Station#571, and an expense of $31.28 on February 6 at Gas Station #456.

As described above, the identification of the gas station expenses canbe based on various factors, e.g., identifying MCCs associated with thetransaction, text-based analysis (identifying words like “gas” or“station”), transactional pattern analysis (gas expenses occur everyseven days or so). Upon identifying or filtering out all gas-relatedexpenses from the transaction data in the card statement 300, anapproximate number of miles driven on the vehicle within that timeperiod (January 13 to February 12) may be calculated, which will befurther described below.

FIG. 4 illustrates an example calculation 400 of miles driven on avehicle based on transaction data from card statement 300 according toone or more embodiments. For the calculation 400, information relatingto average MPG of the vehicle and gas prices on the dates correspondingto the gas expenses may be obtained or provided, for example, byexternal or third-party sources, as described above.

As shown, the vehicle may get 23 miles per gallon in the city and 34miles per gallon on the highway. In examples, the two MPGs may beaveraged to get 28.5 miles to the gallon. Further shown in FIG. 4 arethe gas prices on the days gas was purchased. For instance, regular gasat the #456 station on January 13 was $2.85 per gallon. In anotherinstance, regular gas at the #571 station on January 21 was $2.80 pergallon. In a yet another instance, regular gas at the #571 station onJanuary 28 was $2.74 per gallon. And in a further instance, regular gasat the #456 station on February 6 was $2.88 per gallon. It may beunderstood that the octane recommendation corresponding to the make,model, and year of the vehicle be used at least for determining the gasprices.

Accordingly, based on the above information and the gas expenses derivedfrom the card statement 300, the number of miles driven on the vehiclemay be calculated for approximately each week of the month. For thedistance calculation from January 13 to January 21, the gas expense of$35.56 on January 13 can be divided by $2.85 (price of regular gas atthe #456 station on January 13) to get 12.477 gallons, which can bemultiplied by the average MPG of 28.5 to get 355.6 miles (e.g., milesdriven from date of fill-up on January 13 to date of next fill-up onJanuary 21).

For the distance calculation from January 21 to January 28, the gasexpense of $32.62 on January 21 can be divided by $2.80 (price ofregular gas at the #571 station on January 21) to get 11.65 gallons,which can be multiplied by the average MPG of 28.5 to get 332.025 miles(e.g., miles driven from date of fill-up on January 21 to date of nextfill-up on January 28).

Similarly, for the distance calculation from January 28 to February 6,the gas expense of $33.50 on January 28 can be divided by $2.74 (priceof regular gas at the #571 station on January 28) to get 12.226 gallons,which can be multiplied by the average MPG of 28.5 to get 348.449 miles(e.g., miles driven from date of fill-up on January 28 to date of nextfill-up on February 6).

For the distance calculation from February 6 to date of next fill-up,the gas expense of $31.28 on February 6 can be divided by $2.88 (priceof regular gas at the #456 station on February 6) to get 10.861 gallons,which can be multiplied by the average MPG of 28.5 to get 309.452 miles(e.g., miles driven from date of fill-up on February 6 to date of nextfill-up).

Adding the calculated distances for each week, it can be estimated thatapproximately 1,345.616 miles were driven on the vehicle during therelevant month. According to embodiments, similar calculations may beperformed for subsequent months to obtain the total distance driven fora whole year. Moreover, if possible, distance calculations can beconducted any time period with available transaction data so as tobetter predict total distance driven. It may be understood that whencustomer transaction data is unavailable for certain periods of time orthe customer has had the card for a short period of time, distancecalculations for a given month may be used to make year-basedprojections.

In FIG. 4 , for instance, if the monthly credit card statement 300 wasall the real transaction data that was available for the user, a vehicledepreciation prediction system may project that the customer may drive,on average, approximately 16,147.392 miles on the vehicle per year.Based at least in part on this calculation, the prediction system maydynamically determine in real-time a specific depreciation factor forthe vehicle (e.g., $0.25 per driven mile, which equates to depreciationof $4,036.85 per year). For example, depreciation information found inthird-party industry price guides may be used to determine thedepreciation factor. Thus, if more miles are drive on the vehicle, thedepreciation factor may increase.

Using the depreciation factor, a present value of the vehicle may beaccurately predicted by the system, which would otherwise be unknown orunascertainable without direct input from the customer (which, eventhen, could be inaccurate or unreliable). As described above, thedistance, depreciation, and present-value calculations or predictionscan all be handled or performed by a machine learning component of thesystem. The machine learning component may continually learn and adjustpredictions based on new information about the vehicle, such as anactual sale value of the vehicle versus the predicted value, etc. Basedon these adjustments, the machine learning model may dynamically and inreal-time continually maintain an accurate depreciation factor, andthus, an accurate present value.

FIG. 5 illustrates at least two different auto financing products 500according to one or more embodiments. If a predicted present value of avehicle, for instance, meets one or more predefined thresholds (e.g.,threshold price of $7,500 or below), various auto financing products(e.g., new loan offer, refinancing offer, trade-in offer, etc.) may beoffered to the customer via different channels (e.g., e-mail, textmessage, app notification). As shown, an offer for financing a newvehicle may be presented to the customer in at least two ways.

In embodiments, an e-mail 504 may be displayed on mobile device 502,e.g., smartphone belonging to the customer. The e-mail 504 may include a“new vehicle offer” along with a hyperlink that directs the customer tothe offer. Moreover, the e-mail 504 may include various details relatingto the vehicle, such as the present value of the vehicle and that itappears the customer may be in the market for a new vehicle.

In another example, the mobile device 502 may display a website 522,which may contain at least website content 524. As illustrated, the newvehicle offer 526 may be presented in the form of a banner advertisementarranged at the top of the website 522. Similar to the above describedhyperlink, the customer may be directed to the new vehicle offer 526 bytouching the banner ad.

FIG. 6 illustrates a flow diagram 600 in accordance with one or moreembodiments. The flow diagram 600 is related to the prediction ofvehicle depreciation factor and present value and the generation ofproduct(s) based on the predicted vehicle depreciation factor andpresent value. The flow diagram 600 may be implemented by a system, forexample, the vehicle depreciation prediction system 100 of FIG. 1 . Itmay be understood that the features associated with the illustratedblocks may be performed or executed by one or more computing devicesand/or processing circuitry contained therein and further may beunderstood that the blocks are not limited to any specific order and/ormay be executed simultaneously or near simultaneously.

At block 602, customer transaction data may be received. As set forthabove, the customer transaction data may include expenses derived from acustomer's monthly card statement. Thereafter, it may be determinedwhich of the expenses are fuel expenses based on an analysis of theexpenses, such as analyzing the MCC of each expense, analyzing textualdescriptors of the expenses, and even analyzing patterns or trends inthe spending habit of the customer (e.g., the customer purchases everyFriday morning).

At block 604, various information about the customer's vehicle may bedetermined, such as the make, model, and year of the vehicle, fuelconsumption information (e.g., city MPG, highway MPG, average MPG), andcost corresponding to each identified fuel expense (e.g., cost pergallon at a particular gas station on the particular day of fuelpurchase).

At block 606, using the information obtain at block 604, a predictednumber of miles driven on the vehicle may be determined. In someexamples, as described above, if the available or identifiable fuelexpense information in the card statements is limited for the customer,the predicted number of miles driven determined at block 606 may beprojected out to a predefined period of time, such as a year or severalyears.

At block 608, based on the predicted miles driven on the vehicledetermined at block 606, a depreciation factor associated with thevehicle may be determined. In examples, the depreciation factor may beexpressed as depreciation per driven mile (e.g., $0.25 per mile). Asdescribed above, a machine learning model may continually adjust inreal-time or near real-time the calculated depreciation factor based onreal-world data (e.g., based on an actual sale of the vehicle andcomparing the actual sale value to the predicted values).

At block 610, a present value of the vehicle may be predicted based onthe depreciation factor determined or calculated at block 608. Thepresent predicted value of the vehicle may be compared againstpredefined or predetermined threshold values such that a determinationcan be made as to whether one or more auto financing products should begenerated and provided to the customer.

FIG. 7 illustrates an example computing architecture 700, e.g., of acomputing device, such as a desktop computer, laptop, tablet computer,mobile computer, smartphone, etc., suitable for implementing variousembodiments as previously described. In one embodiment, the computingarchitecture 700 may include or be implemented as part of a system,which will be further described below. In examples, the computing deviceand/or the processing circuitries thereof may be configured to at leastexecute, support, provide, and/or access the various features andfunctionalities of the vehicle depreciation prediction system 100 (e.g.,the customer identification engine, the filter engine, the vehicleinformation engine, the machine learning component with the depreciationcalculation engine and the distance calculation engine, the productengine etc.). In addition to the system, it may be understood that thecomputing device and/or the processing circuitries may also beconfigured to perform, support, or execute any of the features,functionalities, descriptions described anywhere herein.

As used in this application, the terms “system” and “component” areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution, examples of which are provided by the exemplary computingarchitecture 700. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical and/or magnetic storage medium), anobject, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution, and a component canbe localized on one computer and/or distributed between two or morecomputers. Further, components may be communicatively coupled to eachother by various types of communications media to coordinate operations.The coordination may involve the uni-directional or bi-directionalexchange of information. For instance, the components may communicateinformation in the form of signals communicated over the communicationsmedia. The information can be implemented as signals allocated tovarious signal lines. In such allocations, each message is a signal.Further embodiments, however, may alternatively employ data messages.Such data messages may be sent across various connections. Exemplaryconnections include parallel interfaces, serial interfaces, and businterfaces.

The computing architecture 700 includes various common computingelements, such as one or more processors, multi-core processors,co-processors, memory units, chipsets, controllers, peripherals,interfaces, oscillators, timing devices, video cards, audio cards,multimedia input/output (I/O) components, power supplies, and so forth.The embodiments, however, are not limited to implementation by thecomputing architecture 700.

As shown in FIG. 7 , the computing architecture 700 includes processor704, a system memory 706 and a system bus 708. The processor 704 can beany of various commercially available processors, processing circuitry,central processing unit (CPU), a dedicated processor, afield-programmable gate array (FPGA), etc.

The system bus 708 provides an interface for system componentsincluding, but not limited to, the system memory 706 to the processor704. The system bus 708 can be any of several types of bus structurethat may further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. Interface adapters may connectto the system bus 708 via slot architecture. Example slot architecturesmay include without limitation Accelerated Graphics Port (AGP), CardBus, (Extended) Industry Standard Architecture ((E)ISA), Micro ChannelArchitecture (MCA), NuBus, Peripheral Component Interconnect (Extended)(PCI(X)), PCI Express, Personal Computer Memory Card InternationalAssociation (PCMCIA), and the like.

The computing architecture 700 may include or implement various articlesof manufacture. An article of manufacture may include acomputer-readable storage medium to store logic. Examples of acomputer-readable storage medium may include any tangible media capableof storing electronic data, including volatile memory or non-volatilememory, removable or non-removable memory, erasable or non-erasablememory, writeable or re-writeable memory, and so forth. Examples oflogic may include executable computer program instructions implementedusing any suitable type of code, such as source code, compiled code,interpreted code, executable code, static code, dynamic code,object-oriented code, visual code, and the like. Embodiments may also beat least partly implemented as instructions contained in or on anon-transitory computer-readable medium, which may be read and executedby one or more processors to enable performance of the operationsdescribed herein.

The system memory 706 may include various types of computer-readablestorage media in the form of one or more higher speed memory units, suchas read-only memory (ROM), random-access memory (RAM), dynamic RAM(DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), staticRAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, polymermemory such as ferroelectric polymer memory, ovonic memory, phase changeor ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS)memory, magnetic or optical cards, an array of devices such as RedundantArray of Independent Disks (RAID) drives, solid state memory devices(e.g., USB memory, solid state drives (SSD) and any other type ofstorage media suitable for storing information. In the illustratedembodiment shown in FIG. 7 , the system memory 706 can includenon-volatile memory 710 and/or volatile memory 712. A basic input/outputsystem (BIOS) can be stored in the non-volatile memory 710.

The computer 702 may include various types of computer-readable storagemedia in the form of one or more lower speed memory units, including aninternal (or external) hard disk drive (HDD) 714, a magnetic floppy diskdrive (FDD) 716 to read from or write to a removable magnetic disk 718,and an optical disk drive 720 to read from or write to a removableoptical disk 722 (e.g., a CD-ROM or DVD). The HDD 714, FDD 716 andoptical disk drive 720 can be connected to the system bus 708 by a HDDinterface 724, an FDD interface 726 and an optical drive interface 728,respectively. The HDD interface 724 for external drive implementationscan include at least one or both of Universal Serial Bus (USB) and IEEE1394 interface technologies.

The drives and associated computer-readable media provide volatileand/or nonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For example, a number of program modules canbe stored in the drives and memory units 710, 712, including anoperating system 730, one or more application programs 732, otherprogram modules 734, and program data 736. In one embodiment, the one ormore application programs 732, other program modules 734, and programdata 736 can include, for example, the various applications and/orcomponents of the system 800.

A user can enter commands and information into the computer 702 throughone or more wire/wireless input devices, for example, a keyboard 738 anda pointing device, such as a mouse 740. Other input devices may includemicrophones, infra-red (IR) remote controls, radio-frequency (RF) remotecontrols, game pads, stylus pens, card readers, dongles, finger printreaders, gloves, graphics tablets, joysticks, keyboards, retina readers,touch screens (e.g., capacitive, resistive, etc.), trackballs, trackpads, sensors, styluses, and the like. These and other input devices areoften connected to the processor 704 through an input device interface742 that is coupled to the system bus 708 but can be connected by otherinterfaces such as a parallel port, IEEE 1394 serial port, a game port,a USB port, an IR interface, and so forth.

A monitor 744 or other type of display device is also connected to thesystem bus 708 via an interface, such as a video adaptor 746. Themonitor 744 may be internal or external to the computer 702. In additionto the monitor 744, a computer typically includes other peripheraloutput devices, such as speakers, printers, and so forth.

The computer 702 may operate in a networked environment using logicalconnections via wire and/or wireless communications to one or moreremote computers, such as a remote computer 748. The remote computer 748can be a workstation, a server computer, a router, a personal computer,portable computer, microprocessor-based entertainment appliance, a peerdevice or other common network node, and typically includes many or allthe elements described relative to the computer 702, although, forpurposes of brevity, only a memory/storage device 750 is illustrated.The logical connections depicted include wire/wireless connectivity to alocal area network (LAN) 752 and/or larger networks, for example, a widearea network (WAN) 754. Such LAN and WAN networking environments arecommonplace in offices and companies, and facilitate enterprise-widecomputer networks, such as intranets, all of which may connect to aglobal communications network, for example, the Internet.

When used in a LAN networking environment, the computer 702 is connectedto the LAN 752 through a wire and/or wireless communication networkinterface or adaptor 756. The adaptor 756 can facilitate wire and/orwireless communications to the LAN 752, which may also include awireless access point disposed thereon for communicating with thewireless functionality of the adaptor 756.

When used in a WAN networking environment, the computer 702 can includea modem 758, or is connected to a communications server on the WAN 754or has other means for establishing communications over the WAN 754,such as by way of the Internet. The modem 758, which can be internal orexternal and a wire and/or wireless device, connects to the system bus708 via the input device interface 742. In a networked environment,program modules depicted relative to the computer 702, or portionsthereof, can be stored in the remote memory/storage device 750. It willbe appreciated that the network connections shown are exemplary andother means of establishing a communications link between the computerscan be used.

The computer 702 is operable to communicate with wire and wirelessdevices or entities using the IEEE 802 family of standards, such aswireless devices operatively disposed in wireless communication (e.g.,IEEE 802.11 over-the-air modulation techniques). This includes at leastWi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wirelesstechnologies, among others. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices. Wi-Fi networks use radiotechnologies called IEEE 802.118 (a, b, g, n, etc.) to provide secure,reliable, fast wireless connectivity. A Wi-Fi network can be used toconnect computers to each other, to the Internet, and to wire networks(which use IEEE 802.3-related media and functions).

The various elements of the devices as previously described withreference to FIGS. 1-6 may include various hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude devices, logic devices, components, processors, microprocessors,circuits, processors, circuit elements (e.g., transistors, resistors,capacitors, inductors, and so forth), integrated circuits, applicationspecific integrated circuits (ASIC), programmable logic devices (PLD),digital signal processors (DSP), field programmable gate array (FPGA),memory units, logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. Examples of software elements mayinclude software components, programs, applications, computer programs,application programs, system programs, software development programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof. However,determining whether an embodiment is implemented using hardware elementsand/or software elements may vary in accordance with any number offactors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints, as desired for a given implementation.

FIG. 8 is a block diagram depicting an example communicationsarchitecture 800 suitable for implementing various embodiments. Forexample, one or more computing devices may communicate with each othervia a communications framework, such as a network. At least onecomputing devices connected to the network may be a user computingdevice, such as a desktop computer, laptop, tablet computer, smartphone,etc. The user, for example, may be a customer or the like. At least asecond computing device connected to the network may be one or moreserver computers, which may be implemented as a back-end server or acloud-computing server. For example, the vehicle depreciation predictionsystem may be provisioned on one or more of the back-end servercomputers. In examples, the user computing device (e.g., customerdevice) may receive the auto financing products from the product engineof the system via the communications framework.

The communications architecture 800 includes various commoncommunications elements, such as a transmitter, receiver, transceiver,radio, network interface, baseband processor, antenna, amplifiers,filters, power supplies, and so forth. The embodiments, however, are notlimited to implementation by the communications architecture 800.

As shown in FIG. 8 , the communications architecture 800 includes one ormore clients 802 and servers 804. The one or more clients 802 and theservers 804 are operatively connected to one or more respective clientdata stores 806 and server data stores 807 that can be employed to storeinformation local to the respective clients 802 and servers 804, such ascookies and/or associated contextual information.

The clients 802 and the servers 804 may communicate information betweeneach other using a communication framework 810. The communicationsframework 810 may implement any well-known communications techniques andprotocols. The communications framework 810 may be implemented as apacket-switched network (e.g., public networks such as the Internet,private networks such as an enterprise intranet, and so forth), acircuit-switched network (e.g., the public switched telephone network),or a combination of a packet-switched network and a circuit-switchednetwork (with suitable gateways and translators).

The communications framework 810 may implement various networkinterfaces arranged to accept, communicate, and connect to acommunications network. A network interface may be regarded as aspecialized form of an input/output (I/O) interface. Network interfacesmay employ connection protocols including without limitation directconnect, Ethernet (e.g., thick, thin, twisted pair 10/100/1000 Base T,and the like), token ring, wireless network interfaces, cellular networkinterfaces, IEEE 802.7a-x network interfaces, IEEE 802.16 networkinterfaces, IEEE 802.20 network interfaces, and the like. Further,multiple network interfaces may be used to engage with variouscommunications network types. For example, multiple network interfacesmay be employed to allow for the communication over broadcast,multicast, and unicast networks. Should processing requirements dictatea greater amount speed and capacity, distributed network controllerarchitectures may similarly be employed to pool, load balance, andotherwise increase the communicative bandwidth required by clients 802and the servers 804. A communications network may be any one and thecombination of wired and/or wireless networks including withoutlimitation a direct interconnection, a secured custom connection, aprivate network (e.g., an enterprise intranet), a public network (e.g.,the Internet), a Personal Area Network (PAN), a Local Area Network(LAN), a Metropolitan Area Network (MAN), an Operating Missions as Nodeson the Internet (OMNI), a Wide Area Network (WAN), a wireless network, acellular network, and other communications networks.

The components and features of the devices described above may beimplemented using any combination of discrete circuitry, applicationspecific integrated circuits (ASICs), logic gates and/or single chiparchitectures. Further, the features of the devices may be implementedusing microcontrollers, programmable logic arrays and/or microprocessorsor any combination of the foregoing where suitably appropriate. It isnoted that hardware, firmware and/or software elements may becollectively or individually referred to herein as “logic” or “circuit.”

At least one computer-readable storage medium may include instructionsthat, when executed, cause a system to perform any of thecomputer-implemented methods described herein.

Some embodiments may be described using the expression “one embodiment”or “an embodiment” along with their derivatives. These terms mean that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment.Moreover, unless otherwise noted the features described above arerecognized to be usable together in any combination. Thus, any featuresdiscussed separately may be employed in combination with each otherunless it is noted that the features are incompatible with each other.

With general reference to notations and nomenclature used herein, thedetailed descriptions herein may be presented in terms of programprocedures executed on a computer or network of computers. Theseprocedural descriptions and representations are used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art.

A procedure is here, and generally, conceived to be a self-consistentsequence of operations leading to a desired result. These operations arethose requiring physical manipulations of physical quantities. Usually,though not necessarily, these quantities take the form of electrical,magnetic or optical signals capable of being stored, transferred,combined, compared, and otherwise manipulated. It proves convenient attimes, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbers,or the like. It should be noted, however, that all of these and similarterms are to be associated with the appropriate physical quantities andare merely convenient labels applied to those quantities.

Further, the manipulations performed are often referred to in terms,such as adding or comparing, which are commonly associated with mentaloperations performed by a human operator. No such capability of a humanoperator is necessary, or desirable in most cases, in any of theoperations described herein, which form part of one or more embodiments.Rather, the operations are machine operations.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. These terms are notnecessarily intended as synonyms for each other. For example, someembodiments may be described using the terms “connected” and/or“coupled” to indicate that two or more elements are in direct physicalor electrical contact with each other. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other.

Various embodiments also relate to apparatus or systems for performingthese operations. This apparatus may be specially constructed for therequired purpose and may be selectively activated or reconfigured by acomputer program stored in the computer. The procedures presented hereinare not inherently related to a particular computer or other apparatus.The required structure for a variety of these machines will appear fromthe description given.

It is emphasized that the Abstract of the Disclosure is provided toallow a reader to quickly ascertain the nature of the technicaldisclosure. It is submitted with the understanding that it will not beused to interpret or limit the scope or meaning of the claims. Inaddition, in the foregoing Detailed Description, it can be seen thatvarious features are grouped together in a single embodiment for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the claimedembodiments require more features than are expressly recited in eachclaim. Rather, as the following claims reflect, inventive subject matterlies in less than all features of a single disclosed embodiment. Thus,the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separateembodiment. In the appended claims, the terms “including” and “in which”are used as the plain-English equivalents of the respective terms“comprising” and “wherein,” respectively. Moreover, the terms “first,”“second,” “third,” and so forth, are used merely as labels, and are notintended to impose numerical requirements on their objects.

What has been described above includes examples of the disclosedarchitecture. It is, of course, not possible to describe everyconceivable combination of components and/or methodologies, but one ofordinary skill in the art may recognize that many further combinationsand permutations are possible. Accordingly, the novel architecture isintended to embrace all such alterations, modifications and variationsthat fall within the spirit and scope of the appended claims.

What is claimed is:
 1. A system comprising: one or more processorsoperable to execute stored instructions that, when executed, cause theone or more processors to: train a machine learning model based at leastin part on customer transaction data, third party data from one or morethird party sources, and real-world data, wherein the customertransaction data includes at least card-based transactions, the thirdparty data includes at least information related to a plurality ofvehicles, and the real-world data includes at least information relatedto real transactional events associated with the plurality of vehicles,the machine learning model dynamically adjusting depreciation or presentvalue calculations based at least in part on the information related tothe real transaction events of the real-world data by at least comparingactual sale prices to previously predicted values of the plurality ofvehicles determined by the machine learning model; receive or access thecustomer transaction data associated with a card belonging to acustomer, the customer transaction data including a plurality ofexpenses; determine which of the plurality of expenses are fuel expensesbased on an analysis of the customer transaction data; determine atleast a make, model, and year of a vehicle associated with the customer;determine fuel consumption information for the make, the model, and theyear of the vehicle; determine a cost corresponding to each determinedfuel expense; determine a predicted number of miles driven on thevehicle based at least in part on: (i) the fuel expenses, (ii) the costcorresponding to each fuel expense, and (iii) the fuel consumptioninformation for the vehicle; determine, via the trained machine learningmodel, a depreciation factor associated with the vehicle based on thepredicted number of miles driven on the vehicle; determine, via thetrained machine learning model, a present value of the vehicle based atleast in part on the determined depreciation factor; determine whetherthe present value of the vehicle falls below a predefined thresholdvalue; generate one or more auto financing products in response to thedetermination that the present value falls below the predefinedthreshold value; and provide the one or more auto financing products tothe customer via one or more channels.
 2. The system of claim 1, whereinthe one or more auto financing products comprise a new loan offer, arefinancing offer, a trade-in offer.
 3. The system of claim 1, whereinthe fuel consumption information comprises a city miles per gallon (MPG)value associated with a city environment and a highway MPG valueassociated with a highway environment and wherein the one or moreprocessors is further caused to average the city MPG value and highwayMPG value to obtain an average MPG associated with the vehicle.
 4. Thesystem of claim 1, wherein the determination of which of the pluralityof expenses in the customer transaction data are fuel expenses comprisesthe one or more processors being further caused to analyze each of theplurality of expenses based on a merchant category code (MCC) andidentify an expense as a fuel expense if the analyzed MCC corresponds toa fuel station MCC or a fuel-related MCC.
 5. The system of claim 1,wherein the determination of which of the plurality of expenses in thecustomer transaction data are fuel expenses comprises the one or moreprocessors being further caused to analyze each of the plurality ofexpenses based on textual descriptors and identify an expense as a fuelexpense if the analyzed textual descriptors indicate that the fuelexpense is related to a fuel station or fuel.
 6. The system of claim 1,wherein the determination of which of the plurality of expenses in thecustomer transaction data are fuel expenses comprises the one or moreprocessors being further caused to analyze a pattern or a trend in theplurality of expenses and identify an expense as a fuel expense based onthe analyzed pattern or the analyzed trend in the plurality of expenses.7. The system of claim 1, wherein the plurality of expenses is derivedfrom a monthly card statement associated with the card and the predictednumber of miles driven corresponds to a time period associated with themonthly card statement.
 8. The system of claim 7, wherein the one ormore processors is further caused to determine a total number of milesdriven on the vehicle in a year by projecting out the predicted numberof miles driven.
 9. The system of claim 1, wherein the machine learningmodel is a classification model, a linear regression model, a logisticregression model, a decision tree model, a support vector machine (SVM)model, a Naive Bayes model, a k-nearest neighbors (kNN) model, k-meansmodel, a random forest model, a dimensionality reduction algorithm, agradient boosting algorithm, an XGBoost algorithm, a LightGBM algorithm,or a Catboost model.
 10. The system of claim 1, wherein the one or moreprocessors is further caused to receive real-world data related to thevehicle and cause the machine learning model to learn or adjust thedepreciation factor of the vehicle based on the real-world data.
 11. Amethod comprising: training a machine learning model based at least inpart on customer transaction data, third party data from one or morethird party sources, and real-world data, wherein the customertransaction data includes at least card-based transactions, the thirdparty data includes at least information related to a plurality ofvehicles, and the real-world data includes at least information relatedto real transactional events associated with the plurality of vehicles,the machine learning model dynamically adjusting depreciation or presentvalue calculations based at least in part on the information related tothe real transaction events of the real-world data including actual saleprices for previously predicted values of the plurality of vehiclesdetermined by the machine-learning model; receiving or accessing thecustomer transaction data associated with a card belonging to acustomer, the customer transaction data including a plurality ofexpenses; determining, via one or more processors, which of theplurality of expenses are fuel expenses based on an analysis of thecustomer transaction data; determining at least a make, model, and yearof a vehicle associated with the customer; determining fuel consumptioninformation for the make, the model, and the year of the vehicle;determining a cost corresponding to each fuel expense; determining, viathe one or more processors, a predicted number of miles driven on thevehicle based at least in part on: (i) the fuel expenses, (ii) the costcorresponding to each fuel expense, and (iii) the fuel consumptioninformation for the vehicle; determining, via the trained machinelearning model, a depreciation factor associated with the vehicle basedon the predicted number of miles driven on the vehicle; determining, viathe trained machine learning model, a present value of the vehicle basedat least in part on the determined depreciation factor; determining, viathe one or more processors, whether the present value of the vehiclefalls below a predefined threshold value; generating one or more autofinancing products based on the determination that the present valuefalls below the predefined threshold value; and providing the one ormore auto financing products to the customer via one or more channels.12. The method of claim 11, wherein the one or more auto-financingproducts comprising a new loan offer, a refinancing offer, a trade-inoffer or a combination thereof.
 13. The method of claim 12, furthercomprising: providing the one or more auto-financing with an offer forfinancing a new vehicle.
 14. The method of claim 11, wherein thecustomer is both a card customer and an auto financing customer.
 15. Themethod of claim 11, further comprising: receiving real-world datarelated to the vehicle; and causing the machine learning model to learnor adjust the depreciation factor of the vehicle based on the real-worlddata.
 16. A non-transitory computer-readable storage medium storingcomputer-readable program code executable by at least one processor to:train a machine learning model based at least in part on customertransaction data, third party data from one or more third party sources,and real-world data, wherein the customer transaction data includes atleast card-based transactions, the third party data includes at leastinformation related to a plurality of vehicles, and the real-world dataincludes at least information related to real transactional eventsassociated with the plurality of vehicles, the machine learning modeldynamically adjusting depreciation or present value calculations basedat least in part on the information related to the real transactionevents of the real-world data by at least comparing actual sale pricesto corresponding previously predicted values of the plurality ofvehicles determined by the machine-learning model; receive or access thecustomer transaction data associated with a card belonging to acustomer, the customer transaction data including a plurality ofexpenses; determine which of the plurality of expenses are fuel expensesbased on an analysis of the customer transaction data; determine atleast a make, model, and year of a vehicle associated with the customer;determine fuel consumption information for the make, the model, and theyear of the vehicle; determine a cost corresponding to each determinedfuel expense; determine a predicted number of miles driven on thevehicle based at least in part on: (i) the fuel expenses, (ii) the costcorresponding to each fuel expense, and (iii) the fuel consumptioninformation for the vehicle; determine, via the trained machine learningmodel, a depreciation factor associated with the vehicle based on thepredicted number of miles driven on the vehicle; determine, via thetrained machine learning model, a present value of the vehicle based atleast in part on the determined depreciation factor; determine whetherthe present value of the vehicle falls below a predefined thresholdvalue; generate one or more auto financing products based on thedetermination that the present value falls below the predefinedthreshold value; and provide the one or more auto financing products tothe customer via one or more channels.
 17. The non-transitorycomputer-readable storage medium of claim 16, wherein the one or moreauto-financing products comprising a new loan offer, a refinancingoffer, a trade-in offer, or a combination thereof.
 18. Thenon-transitory computer-readable storage medium of claim 17, wherein thecomputer-readable program code further causes the at least one processorto: providing the one or more auto-financing with an offer for financinga new vehicle.
 19. The non-transitory computer-readable storage mediumof claim 16, wherein the machine learning model is a classificationmodel, a linear regression model, a logistic regression model, adecision tree model, a support vector machine (SVM) model, a Naive Bayesmodel, a k-nearest neighbors (kNN) model, k-means model, a random forestmodel, a dimensionality reduction algorithm, a gradient boostingalgorithm, an XGBoost algorithm, a LightGBM algorithm, or a Catboostmodel.
 20. The non-transitory computer-readable storage medium of claim16, wherein the computer-readable program code further causes the atleast one processor to: receive real-world data related to the vehicle;and cause the machine learning model to learn or adjust the depreciationfactor of the vehicle based on the real-world data.